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Journal of Information Science and Engineering, Vol. 20 No. 6, pp. 1141-1159 (November 2004)

A Computational Approach to Discover Differential Cooperation of Regulatory Sites in Functionally Related Genes in Yeast Genome

Hsien-Da Huang, Jorng-Tzong Horng1,2, Chia-Hui Chang1,
Tsung-Shan Tsou
3, Jing-Yue Hong* and Baw-Jhiune Liu*
Department of Biological Science and Technology
Institute of Bioinformatics
National Chiao Tung University
Hsinchu, 300 Taiwan
1Department of Computer Science and Information Engineering
2Department of Life Science
3Institute of Statistics
National Central University
Chungli, 320 Taiwan
E-mail: horng@db.csie.ncu.edu.tw
*Department of Computer Science and Engineering
Yuan Ze University
Chungli, 320 Taiwan

The availability of genome-wide gene expression data provides a unique set of genes from which to decipher the mechanisms underlying the common transcriptional response. A set of transcription factors which bind to target sites regulate the gene transcription cooperatively. This motivates us to discover the site associations of known transcription factor binding sites and certain repetitive elements. Those over-represented repetitive elements in the promoter regions of functionally related genes are predicted as putative regulatory elements. The study is to analyze how the differential site associations of the known regulatory sites and putative regulatory elements are distributed in the promoter regions of groups of functionally related genes. The functional-specific site associations are discovered by a statistical approach and the over-represented repetitive elements involving in the site associations are possible to be transcription factor binding sites. The site associations facilitate to predict functional-specific putative regulatory elements and to identify genes potentially co-regulated by the putative regulatory elements. Our proposed approach is applied to Saccharomyces cerevisiae and the promoter regions of yeast ORFs.

Keywords: regulatory site, transcription factor, yeast, data mining, promoter

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Received November 1, 2002; revised June 10, 2003; accepted August 5, 2003.
Communicated by Chuen-Tsai Sun.